Decentralizing Air Traffic Flow Management with Blockchain-based Reinforcement Learning

Duong Ta; Ketan Kumar Todi; Umang Chaudhary; Linh Truong

doi:10.1109/INDIN41052.2019.8972225

Decentralizing Air Traffic Flow Management with Blockchain-based Reinforcement Learning

Duong Ta, Ketan Kumar Todi, Umang Chaudhary, Linh Truong

Department of Computer Science

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

22 Citations (Scopus)

Abstract

We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness of our approach. To the best of our knowledge, this is the first work that considers blockchain-based, distributed RL for ATFM.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
Publisher	IEEE
Pages	1795-1800
Number of pages	8
ISBN (Electronic)	978-1-7281-2927-3
DOIs	https://doi.org/10.1109/INDIN41052.2019.8972225
Publication status	Published - 2019
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Industrial Informatics - Aalto University, Helsinki and Espoo, Finland Duration: 22 Jul 2019 → 25 Jul 2019 Conference number: 17 https://www.indin2019.org/

Conference

Conference	IEEE International Conference on Industrial Informatics
Abbreviated title	INDIN
Country/Territory	Finland
City	Helsinki and Espoo
Period	22/07/2019 → 25/07/2019
Internet address	https://www.indin2019.org/

Keywords

Air traffic flow management
Blockchain
Decentralized optimization
Multi-agent systems
Reinforcement learning

Access to Document

10.1109/INDIN41052.2019.8972225

https://ink.library.smu.edu.sg/sis_research/4647/

Cite this

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title = "Decentralizing Air Traffic Flow Management with Blockchain-based Reinforcement Learning",

abstract = "We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness of our approach. To the best of our knowledge, this is the first work that considers blockchain-based, distributed RL for ATFM.",

keywords = "Air traffic flow management, Blockchain, Decentralized optimization, Multi-agent systems, Reinforcement learning",

author = "Duong Ta and Todi, \{Ketan Kumar\} and Umang Chaudhary and Linh Truong",

year = "2019",

doi = "10.1109/INDIN41052.2019.8972225",

language = "English",

pages = "1795--1800",

booktitle = "Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019",

publisher = "IEEE",

address = "United States",

note = "IEEE International Conference on Industrial Informatics, INDIN ; Conference date: 22-07-2019 Through 25-07-2019",

url = "https://www.indin2019.org/",

}

Ta, D, Todi, KK, Chaudhary, U & Truong, L 2019, Decentralizing Air Traffic Flow Management with Blockchain-based Reinforcement Learning. in Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019., 8972225, IEEE, pp. 1795-1800, IEEE International Conference on Industrial Informatics, Helsinki and Espoo, Finland, 22/07/2019. https://doi.org/10.1109/INDIN41052.2019.8972225

Decentralizing Air Traffic Flow Management with Blockchain-based Reinforcement Learning. / Ta, Duong; Todi, Ketan Kumar; Chaudhary, Umang et al.
Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019. IEEE, 2019. p. 1795-1800 8972225.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Decentralizing Air Traffic Flow Management with Blockchain-based Reinforcement Learning

AU - Ta, Duong

AU - Todi, Ketan Kumar

AU - Chaudhary, Umang

AU - Truong, Linh

N1 - Conference code: 17

PY - 2019

Y1 - 2019

N2 - We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness of our approach. To the best of our knowledge, this is the first work that considers blockchain-based, distributed RL for ATFM.

AB - We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness of our approach. To the best of our knowledge, this is the first work that considers blockchain-based, distributed RL for ATFM.

KW - Air traffic flow management

KW - Blockchain

KW - Decentralized optimization

KW - Multi-agent systems

KW - Reinforcement learning

UR - https://www.scopus.com/pages/publications/85079043348

U2 - 10.1109/INDIN41052.2019.8972225

DO - 10.1109/INDIN41052.2019.8972225

M3 - Conference article in proceedings

SP - 1795

EP - 1800

BT - Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019

PB - IEEE

T2 - IEEE International Conference on Industrial Informatics

Y2 - 22 July 2019 through 25 July 2019

ER -

Decentralizing Air Traffic Flow Management with Blockchain-based Reinforcement Learning

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Keywords

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